195,535 datasets found

The SWSR dataset consists of two files: SexWeibo.csv and SexComment.csv, containing weibos (posts) and comments (replies) respectively.

The MS MARCO Dev (small) dataset is a small version of the MS MARCO passage dev set.

The RetroMAE dataset is used for pre-training retrieval-oriented language models.

The TREC 2020 Deep Learning (Passage Subtask) dataset consists of 54 queries with manual judgments from NIST annotators (211 relevance assessments per query, on average).

The TREC 2019 Deep Learning (Passage Subtask) dataset consists of 43 manually-judged queries using four relevance grades (215 relevance assessments per query, on average).

The dataset used in the paper is a set of waypoints and edges for planning.

The dataset used in the paper is a 2D environment where experiments are done.

A dataset for training the skinning weight network with neutral garments and ground truth skinning weights.

The proposed system consists of four main modules: Monitoring, Optimal AutoML, Detection, and Mitigation.

The dataset used in this paper consists of three tasks: Multi-Genre Natural Language Inference (MNLI), Quora Question Pairs (QQP), and Stanford Sentiment Treebank (SST-2).

Larger language models have higher accu- racy on average, but are they better on ev- ery single instance (datapoint)?

NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving

The dataset used in this paper for efficient industrial refrigeration scheduling with peak pricing.

The Animals with Attributes (AwA) dataset consists of 50 classes of animals, with 30,475 images in total.

A benchmark for optical flow estimation.

Optical flow estimation on edge devices while ensuring high accuracy.

The dataset used in this study is a collection of molecular fingerprints generated by pre-trained models based on natural language processing algorithms.

The Admetlab 2.0 dataset is an integrated online platform for accurate and comprehensive predictions of admet properties.

The Global Data Association for Multi-Object Tracking using Network Flows dataset is used to evaluate the performance of multi-object tracking algorithms.

The proposed Tracking-by-Animation (TBA) framework achieves unsupervised end-to-end learning of MOT tasks.